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The Dog Days of PySpark

 

PySpark. One of those things to hate and love, well … kinda hard not to love. PySpark is the abstraction that lets a bazillion Data Engineers forget about that blight Scala and cuddle their wonderfully soft and ever-kind Python code, while choking down gobs of data like some Harkonnen glutton.

But, that comes with a price. The price of our own laziness and that idea that all that glitters is gold, to take the easy path. One of the main problems is the dreadful mistake of mixing native Python in with your PySpark and expecting things to go fine at scale. Which it most assuredly will not.

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What is a Data Mesh?

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Data Types in Delta Lake + Spark. Join and Storage Performance.

Hmm … data types. We all know they are important, but we don’t take them very seriously. I mean we know the difference between boolean, string, and integers, those are easy to get right. But we all get sloppy, sometimes we got the string and varchar route because we don’t spend enough time on the data model to care.

Can a string versus a int or bigint in Delta Lake with Spark have a big impact on performance? Data size? Does it matter? Let’s find out.

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Replacing Pandas with Polars. A Practical Guide.

Photo by Stone Wang on Unsplash

I remember those days, oh so long ago, it seems like another lifetime. I haven’t used Pandas in many a year, decades, or whatever. We’ve all been there, done that. Pandas I mean. I would dare say it’s a rite of passage for most data folk. For those using Python, it’s probably one of the first packages you use other than say … requests?

You know, Pandas feels like Airflow, everyone keeps talking about its demise, but there it is everywhere … used by everyone. Sure it’s old, wrinkled, annoying, slow, and obtuse, but it’s ours, and that makes it the words of Gollum … precious.

We should probably get to the point already. Everyone is talking about Polars. Polars is supposed to replace Pandas. Will it? Maybe 10 years from now. You can’t untangle Pandas from everywhere it exists overnight. Do you still want to replace Pandas with Polars and be one of the cool kids? Ok. Let’s take a look at a practical guide to replacing Pandas with Polars, comparing functionally used by most people. My code is available on GitHub.

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Simplify Delta Lake Complexity with mack.

Anyone who’s been roaming around the forest of Data Engineering has probably run into many of the newish tools that have been growing rapidly around the concepts of Data Warehouses, Data Lakes, and Lake Houses … the merging of the old relational database functionality with TB and PB level cloud-based file storage systems. Tools like Delta Lake, lakeFS, Hudi, and the like.

Sure, these tools have been around for some time, but the uptake and adoption of them all have been rapidly growing. I use Delta Lake on a daily basis, taking advantage of the many wonderful features it provides to simplify and reduce complexity in data pipelines. But, I’ve been sitting around for a long time waiting for the plethora of “add-on” tooling to come out, stuff that will make my life easier. I recently saw one of the first tools like that for Delta Lake, namely mack.

Mack appears to have the ability to “do the hard work for you,” a concept that appears to be growing in popularity, but which I have a fraught relationship with. Double-edged sword? Let’s find out.

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What is Apache Arrow? Asking for a friend.

We’ve all been in that spot, especially in tech. You wanted to fit in, be cool, and look smart, so you didn’t ask any questions. And now it’s too late. You’re stuck. Now you simply can’t ask … you’re too afraid. I get it. Apache Arrow is probably one of those things. It keeps popping up here and there and everywhere.

The only reason I know anything about Arrow is that some years ago, circa 2019 and earlier I stumbled into Arrow and used it to read and write Parquet files (pyarrow that is). Heck, I even used it to tie together Python and Hadoop, Lord knows what I was thinking back then. I’m amazed at how much I used PyArrow back in the day, even to compare Parquet vs Avro.

“Back then it seems like no one used Arrow much, no one was writing about it, using it, or talking about it. At least not that I saw. But oh how times have changed. Arrow seems to be showing up everywhere and is starting to become a backbone for many other tools.”

– me
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Dataframe Showdown – Polars vs Spark vs Pandas vs DataFusion. Guess who wins?

There once was a day when no one used DataFrames that much. Back before Spark had really gone mainstream, Data Scientists were still plinking around with Pandas a lot. My My, what would your mother say? How things have changed. Now everyone wants a piece of the DataFrame pie. I mean it tastes so good, doesn’t it?

Would anyone like a nice big slice of groupBy, maybe agg is what you need? No? Can you say distributed data set? Whatever it is you’re looking for, I’m quite sure a nice old DataFrame can give it to you. With so many options to choose from … what do you choose? I don’t know, whatever works best for you. But, it does set the stage nicely for a clash of the titans per see.

Let’s do this just that. Straight out of the box performance test. Bunch of CSV’s, a little aggregation, just some simple stuff. Mirror mirror on the wall, who is the fastest with DataFrames of them all?

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Why Data Migrations Suck.

Photo by Ray Hennessy on Unsplash

I’ve often wondered what purgatory would be like, doing penance for millennia into eternity. It would probably be doing data migrations. I suppose they are not all that dissimilar from normal software migrations, but there are a few things that make data migrations a little more horrible and soul-sucking. Data migrations are able to slow teams down to a crawl, take at least twice as long as planned, and be way more difficult than imagined.

Can’t it be made easy, shouldn’t Data Migrations have been conquered by now? I mean just put together the perfect plan, break up the work, make a bunch of tickets, estimate the work, and the rest falls into place? If only.

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A Tale of Betrayal and Heartbreak – Databricks Workflows and Jobs.

Photo by Francesco Alberti on Unsplash

Nothing captures the imagination and heart like a tale of betrayal and heartbreak, and that is a tale I want to bring to you today. It’s a tale of Databricks Workflows and Jobs, version changes, new features, API’s, and insidious little hidden gems that will make you pull your hair out when you find them. It’s a tale of what not to do, a tale of how to put developer and customer experience first, instead of forcing unwanted solutions down the throats of the little birdies feeding at your nest.

As a Data Engineering simplicity and ease of use is something close to my heart, something that Databricks did well, or maybe I should say used to do well … before recent releases like Jobs 2.1 API. I hope you can hear the bitterness oozing from my words.

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Introduction to Historical Loads – for Data Engineers.

There are probably few things in life that will strike more fear and tumult in the heart of the Data Engineer than historical loads. You know, on the surface it seems like such an innocent thing. How could it possibly be, just take a bunch of data stored somewhere and shove it into a table. If only. Life never works that way, and neither does the historical load. You would think after decades we all would have figured it out you know. Is it because we don’t do it enough? Maybe it’s like regex, you just figure it out as you go every single time, telling yourself you’ll do it right next time.

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